Generative AI and the rise of deepfake technology: a journey from traditional techniques to LLM integration

Generative artificial intelligence (AI) is advancing rapidly, with deepfake technology illustrating the intersection of AI and multimedia forensics. This study examines the classification and evolution of deepfake, exploring their definition, societal impact, advantages, challenges, and future potential. It traces the development of deepfake from early techniques to modern generative models and analyzes the role of large language models (LLMs) in their creation and detection. The study also emphasizes the emerging role of large vision-language models (LVLMs) in enhancing multimodal deepfake detection. By categorizing deepfake advancements and their enabling AI technologies, this research offers a structured framework for researchers and practitioners. Additionally, it highlights critical risks, including misinformation and the erosion of public trust. To address these concerns, the study advocates for a multidisciplinary mitigation strategy, combining technical, ethical, and policy-based approaches, along with a proposed prevention framework.